Remote AI Data Engineer: Build Scalable Data Pipelines

Carbon Data Solutions; South Africa; Remote; ZAR 600,000 - 1,200,000; Full time

Benefits offered by this job

Remote work

Professional development

Modern data infrastructure

Competitive salary

AI-driven environment

Job summary

Carbon Data Solutions seeks a Remote AI Data Engineer to design and maintain scalable data pipelines for AI/ML initiatives. You will gather, clean, and transform large datasets, enabling model training and analytics in a fully remote setup.

Ideal candidates have a background in data engineering with Python/SQL/Scala, experience with Spark or Hadoop, and familiarity with AWS, Azure, or GCP. A strong data warehousing foundation is required.

Qualifications

• Bachelor's degree or higher in a related field.
• 3+ years of data engineering experience with AI/ML data prep.
• Proficiency in Python, SQL, or Scala.
• Experience with Spark, Hadoop or Kafka.
• Familiarity with cloud data platforms (AWS/Azure/GCP).
• Strong data warehousing concepts and DB tech.

Responsibilities

• Design, build, and maintain scalable data pipelines for AI/ML apps.
• Develop ETL/ELT processes to ingest/transform data.
• Optimize storage and retrieval for large datasets.
• Collaborate with data scientists to meet data needs.
• Implement data governance and quality assurance measures.
• Monitor systems and troubleshoot data availability and integrity.

Skills

Data engineering

AI/ML data preparation

Python

SQL

Scala

Big data concepts

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Spark

Hadoop

Kafka

AWS

Azure

GCP

Job description

Carbon Data Solutions seeks a Remote AI Data Engineer to design and maintain scalable data pipelines for AI/ML initiatives. You will gather, clean, and transform large datasets, enabling model training and analytics in a fully remote setup.

Ideal candidates have a background in data engineering with Python/SQL/Scala, experience with Spark or Hadoop, and familiarity with AWS, Azure, or GCP. A strong data warehousing foundation is required.